Summary Mapping weed densities within crops has conventionally been achieved either by detailed ecological monitoring or by field walking, both of which are time‐consuming and expensive. Recent advances have resulted in increased interest in using Unmanned Aerial Systems (UAS) to map fields, aiming to reduce labour costs and increase the spatial extent of coverage. However, adoption of this technology ideally requires that mapping can be undertaken automatically and without the need for extensive ground‐truthing. This approach has not been validated at large scale using UAS‐derived imagery in combination with extensive ground‐truth data. We tested the capability of UAS for mapping a grass weed, Alopecurus myosuroides, in wheat crops. We addressed two questions: (i) can imagery accurately measure densities of weeds within fields and (ii) can aerial imagery of a field be used to estimate the densities of weeds based on statistical models developed in other locations? We recorded aerial imagery from 26 fields using a UAS. Images were generated using both RGB and Rmod (Rmod 670–750 nm) spectral bands. Ground‐truth data on weed densities were collected simultaneously with the aerial imagery. We combined these data to produce statistical models that (i) correlated ground‐truth weed densities with image intensity and (ii) forecast weed densities in other fields. We show that weed densities correlated with image intensity, particularly Rmod image data. However, results were mixed in terms of out of sample prediction from field‐to‐field. We highlight the difficulties with transferring models and we discuss the challenges for automated weed mapping using UAS technology.

中文翻译：

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评估无人机系统在田间尺度绘制杂草地图的潜力：以 Alopecurus myosuroides 为例


摘要 绘制农作物内的杂草密度通常是通过详细的生态监测或田间行走来实现的，这两种方法既耗时又昂贵。最近的进展导致人们对使用无人机系统 (UAS) 绘制田野地图越来越感兴趣，旨在降低劳动力成本并增加覆盖的空间范围。然而，理想情况下，采用这项技术需要能够自动进行测绘，而不需要进行大量的地面实况验证。这种方法尚未使用无人机衍生的图像与广泛的地面实况数据相结合进行大规模验证。我们测试了无人机在小麦作物中绘制禾本科杂草（Alopecurus myosuroides）地图的能力。我们解决了两个问题：(i) 图像能否准确测量田地内杂草的密度；(ii) 田地的航空图像能否用于根据其他地点开发的统计模型来估计杂草的密度？我们使用 UAS 记录了 26 个区域的航空图像。图像是使用 RGB 和 Rmod (Rmod 670–750 nm) 光谱带生成的。杂草密度的地面实况数据与航空图像同时收集。我们结合这些数据来生成统计模型，该模型（i）将真实杂草密度与图像强度相关联，以及（ii）预测其他领域的杂草密度。我们证明杂草密度与图像强度相关，特别是 Rmod 图像数据。然而，在不同领域的样本外预测方面，结果参差不齐。我们强调了模型传输的困难，并讨论了使用无人机技术进行自动杂草绘图的挑战。